ProteoformTracker: an interactive tool for planning proteoform detectability in top-down and middle-down proteomics.

Authors

Araf Mahmud, Zhihao Zhang, Si Wu, Chen Huang

Published in

bioRxiv : the preprint server for biology. Sep 15, 2026. Epub Sep 15, 2026.

Abstract

Characterizing proteome complexity in disease contexts is essential for understanding molecular mechanisms and advancing therapeutic development. Mass spectrometry (MS)-based top-down and middle-down proteomics (TDP/MDP) can resolve intact proteoforms - protein molecules carrying a unique combination of isoform sequence and post-translational modifications (PTMs); however, their technical complexity and modest throughput present challenges for experimental planning and limit their broader application. Here, we present ProteoformTracker, an online web tool that prospectively models MS signal and evaluates the feasibility of using TDP/MDP to distinguish a target proteoform from related isoforms and the background proteome. ProteoformTracker takes as input a gene's annotated isoforms, a novel long-read/assembled transcript, or an rMATS alternative-splicing event, with or without user-specified PTMs, and predicts each proteoform's MS1 charge-state envelope and exact isotope pattern, scores per-bond MS2 fragmentation propensity, and searches the full reference human proteome for confounding proteins that could share the target's intact mass or a charge-state m/z peak. ProteoformTracker also supports middle-down workflows via simulated partial protease digestion. Results are rendered as interactive, zoomable MS1 and MS2 visualizations with live resolvability and fragment-ion statistics, letting users incorporate outside evidence into which confounders they compare against. We envision ProteoformTracker as a useful tool for users to plan TDP/MDP experiments targeting specific proteoforms.
ProteoformTracker is implemented in R (Shiny) with a Python backend for exact mass and isotope-pattern calculation and is freely available at http://www.proteoformtracker.org together with the documentation and a walkthrough. The source code is available at https://github.com/HuangLabAtUAB/ProteoformTracker under an MIT license.
Supplementary data are available.

PMID:
42780149
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.

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